Description: After research involving auto insurance is retracted due to fraud, I revisit my own book review and unskeptical quoting of that now-debunked research. A lesson in ASOP 23, on Data Quality, ensues, as it seems data in the research was falsified. There is also a lesson in ASOP 56, Modeling, which includes aspects of relying on outside experts for model input. At the end of it all, the value of our actuarial professional standards, in light of other professions seemingly having no standards (or no serious repercussions for breaching said standards), shines through. Also, be skeptical of 'one simple trick' approaches to transform the insurance industry.

Description: To review 2021 and look forward to 2022 as the new Chair of the Actuary of the Future section council

Description: An update to the risk management readers on what the JRMS is planning to do over 2023 to serve its membership.

Description: Wright State University is a public research university in Dayton, Ohio, with approximately 11,000 students. With the lowest tuition fees in Ohio, the university has an access mission and plays a critical role in the region by offering high-quality, affordable education. I joined Wright State University in fall 2019 as the director of the Actuarial Science program. In spring 2020 we attained the Universities and Colleges with Actuarial Programs (UCAP)-AC designation from the Society of Actuaries (SOA), becoming one of only three universities in southwest Ohio at this recognition tier. In this article, I would like to tell you about the journey our program has taken—its successes and its challenges. The goal is to offer a resource to other universities seeking to upgrade their Actuarial Science programs.

Description: Actuaries are leveraging predictive modeling and machine learning more and more as they strive to provide greater value to their stakeholders. Actuarial standards of practice provide guidance around modeling and data. The world has changed significantly over the last few years, so it is important these models be tested for fairness as well as any statistical biases. This article addresses some of the key issues actuaries need to be aware of as Corporations and society work through the responsible us of this technology and underlying data.

Description: Actuary of the Future Chairperson's corner. The article celebrates success of the section in 2023 and describes the 2024 goals and objectives.

Description: An overview of bias and fairness in machine learning models, as they apply to use cases in life insurance.

Description: A discussion with an experienced actuarial manager on how the COVID-19 pandemic has impacted people management and what we can learn and apply in the actuarial profession going forward.

Description: A story about a mother/actuary’s journey of discovering her work-life balance through entrepreneurship.

Description: The paper demonstrates that the most popular rationalization for evolving glide paths is flawed and should be discarded. The paper also demonstrates that glide paths should generally evolve for investors that endeavor to optimize outcomes and reevaluate their portfolios regularly.